A heuristic approach for the online order batching problem with multiple pickers. (October 2021)

• Record Type: Journal Article
• Authors: Gil-Borrás, Sergio; Pardo, Eduardo G.; Alonso-Ayuso, Antonio; Duarte, Abraham
• Is Part Of: Computers & industrial engineering. Volume 160(2021)
• Publication Date: 2021-10
• Subjects: Online Order Batching Problem -- Multiple pickers -- Multistart search -- Variable Neighborhood Descent; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2021.107517
• Languages: English
• ISSNs: 0360-8352

Abstract

Abstract: The Online Order Batching Problem with Multiple Pickers (OOBPMP) consists of optimizing the operations related to the picking process of orders in a warehouse, when the picking policy follows an order batching strategy. In this case, this variant of the well-known Order Batching Problem considers the existence of multiple workers in the warehouse and an online arrival of the orders. We study three different objective functions for the problem: minimizing the completion time, minimizing the picking time, and minimizing the differences in the workload among the pickers. We have identified and classified all previous works in the literature for the OOBPMP. Finally, we propose a multistart procedure hybridized with a Variable Neighborhood Descent metaheuristic to handle the problem. We test our proposal over well-known instances previously reported in the literature by empirically comparing the performance of our proposal with previous methods in the state of the art. The statistical tests corroborated the significance of the results obtained. Highlights: Literature review of the Online Order Batching Problem with multiple pickers (OOBPMP). Analysis of the previous methods in the state of the art for the OOBPMP. A new algorithmic proposal based on metaheuristics for the problem. An empirical study of several objective functions to tackle the OOBPMP. The improvement over previous methods in the literature is supported by statistical tests.